知識圖書館

The article compares C++, Java, C#, Python, MATLAB, and R as routes into software roles in finance. It connects C++ with maintaining older systems, numerical pricing libraries, and trading infrastructure, and describes a further specialization in…

The article derives a no-arbitrage value for a call by constructing a portfolio that combines a long position in the underlying stock with a short call. In its example, the stock starts at 100 and can finish at either 110 or 90; a call with a strike of 100…

The article explains why production quantitative software should generally rely on a maintained numerical library instead of a custom matrix implementation. It introduces Eigen as a C++ option, describing its runtime-sized matrices, dense and sparse…

The article introduces Hidden Markov Models (HMMs) as a way to represent market regimes that cannot be observed directly but affect visible asset returns. Regimes may correspond to changing return behavior, volatility, serial dependence, or correlations. In…

The article explains the Jacobi method for approximating a solution to a square linear system, Ax=b. It splits the matrix into its diagonal component and the remaining entries, then repeatedly updates the estimate using the right-hand side and the previous…

This guide compares five books for learning machine learning through Python, with an emphasis on practical programming. It distinguishes books that teach algorithms through pure Python implementations from those focused on using scikit-learn and related…

The document compares Python threading and multiprocessing for improving simulation performance, with Monte Carlo pricing and strategy backtests as relevant examples. It explains that CPython’s Global Interpreter Lock limits CPU-bound Python threads to one…

The document explains implied volatility as the volatility input that makes a model option price match an observed market price. It motivates volatility quotes as a way to compare options whose premiums are affected by different underlying prices, especially…

The document describes a framework for generating synthetic correlated asset-price paths by combining a correlation-matrix generator with individual time-series models. Independent standard normal shocks are transformed using a matrix factorization so that…

The document explains Itô’s lemma as the stochastic counterpart of the ordinary chain rule. It starts from a drift-diffusion process driven by Brownian motion and describes how to find the differential of a sufficiently smooth function that depends on both…

This tutorial adapts an event-driven trading system to submit orders through Interactive Brokers using the IbPy interface. An execution handler consumes order events, builds broker contract and order objects, assigns incrementing order identifiers, and sends…

This article describes an object-oriented framework for generating synthetic asset-price paths using Geometric Brownian Motion (GBM) and a jump-diffusion process. A shared model interface accepts a starting price, time step, and externally supplied random…

This tutorial implements a long-only moving average crossover strategy in a pandas-based research backtester. It compares a short simple moving average with a longer one, enters when the short average is above the long average, and exits when it falls below.…

This career guide outlines a self-study plan for programmers and technical graduates preparing for quantitative developer roles. It emphasizes that the job is primarily software development: implementing numerical algorithms, building trading infrastructure,…

This overview surveys pre-C++11 Standard Template Library algorithms that operate on ranges through iterators. It groups them by purpose: inspecting elements, transforming or copying values, removing duplicates or matching values, reordering ranges, sorting,…

The document introduces the limit order book as the collection of outstanding buy and sell limit orders. Market orders seek immediate execution and consume available liquidity, while limit orders wait at specified prices and provide liquidity. The best bid…

The document explains how to approximate European vanilla option prices by solving the Black–Scholes partial differential equation with an explicit Euler finite difference scheme. It lays out the PDE domain, expiry payoff, and call boundary conditions, then…

This career guide considers how a software developer in quantitative finance might move into trading or research. It assumes strong programming and engineering skills but less depth in probability, statistics, econometrics, derivatives pricing or…

The article introduces artificial neural networks as computational models inspired by biological neurons, then focuses on the perceptron as an early supervised method for binary classification. It explains that the model combines scalar input features with…

This guide explains how traders can plan the development of software that implements a systematic strategy. It distinguishes codifying rules from automating calculation and execution, then recommends defining trading frequency, instruments, broker…

The document reports a reader survey about which quantitative trading subjects the QuantStart community wanted to study in 2020. Machine learning and deep learning led the responses, followed by mathematical finance and coding and data science. Tactical…

The article develops a supervised learning approach that represents streams of data as paths and uses truncated path signatures as model features. A path signature is a sequence of iterated integrals; the full signature identifies a bounded-variation path up…

This guide surveys Python libraries used across quantitative trading workflows. It groups tools by purpose: NumPy for numerical arrays, Pandas for time-series and tabular data, and TA-Lib for technical indicators; Zipline, PyAlgoTrade, and QSTrader are…

The article explains how cross-validation can estimate a model’s out-of-sample prediction error and help choose its flexibility, using a FTSE 100 forecasting example. Predictors are lagged daily prices or returns, and the response is the next day’s value.…